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Towards an automated decision support system for the identification of additive manufacturing part candidates

Author

Listed:
  • Sheng Yang

    (McGill University)

  • Thomas Page

    (McGill University)

  • Ying Zhang

    (McGill University)

  • Yaoyao Fiona Zhao

    (McGill University)

Abstract

As additive manufacturing (AM) continues to mature, an efficient and effective method to identify parts which are eligible for AM as well as gaining insight on what values it may add to a product is needed. Prior methods are naturally developed and highly experience-dependent, which falls short for its objectiveness and transferability. In this paper, a decision support system (DSS) framework for automatically determining the candidacy of a part or assembly for AM applications is proposed based on machine learning (ML) and carefully selected candidacy criteria. With the goal of supporting efficient candidate screening in the early conceptual design stage, these criteria are further individually decoded to decisive parameters which can be extracted from digital models or resource planning databases. Over 200 existing industrial examples are manually collected and labelled as training data; meanwhile, multiple regression algorithms are tested against each AM potential to find better predictive performance. The proposed DSS framework is implemented as a web application with integrated cloud-based database and ML service, which allows advantages of easy maintenance, upgrade, and retraining of ML models. Two case studies of a hip implant and a throttle pedal are used as demonstrating examples. This preliminary work provides a promising solution for lowering the requirements of non-AM experts to find suitable AM candidates.

Suggested Citation

  • Sheng Yang & Thomas Page & Ying Zhang & Yaoyao Fiona Zhao, 2020. "Towards an automated decision support system for the identification of additive manufacturing part candidates," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 1917-1933, December.
    • Handle: RePEc:spr:joinma:v:31:y:2020:i:8:d:10.1007_s10845-020-01545-6
    DOI: 10.1007/s10845-020-01545-6
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    References listed on IDEAS

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    1. Bogers, Marcel & Hadar, Ronen & Bilberg, Arne, 2016. "Additive manufacturing for consumer-centric business models: Implications for supply chains in consumer goods manufacturing," Technological Forecasting and Social Change, Elsevier, vol. 102(C), pages 225-239.
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    5. Knofius, N. & van der Heijden, M.C. & Zijm, W.H.M., 2019. "Consolidating spare parts for asset maintenance with additive manufacturing," International Journal of Production Economics, Elsevier, vol. 208(C), pages 269-280.
    6. Florinda Matos & Radu Godina & Celeste Jacinto & Helena Carvalho & Inês Ribeiro & Paulo Peças, 2019. "Additive Manufacturing: Exploring the Social Changes and Impacts," Sustainability, MDPI, vol. 11(14), pages 1-18, July.
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    Cited by:

    1. Brylowski, Martin & Schwieger, Lea-Sophie & Nagi, Ayman & Kersten, Wolfgang, 2021. "How to apply artificial intelligence in the additive value chain: A systematic literature review," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Ringle, Christian M. & Blecker, Thorsten (ed.), Adapting to the Future: How Digitalization Shapes Sustainable Logistics and Resilient Supply Chain Management. Proceedings of the Hamburg Internationa, volume 31, pages 65-100, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    2. Ying Zhang & Mutahar Safdar & Jiarui Xie & Jinghao Li & Manuel Sage & Yaoyao Fiona Zhao, 2023. "A systematic review on data of additive manufacturing for machine learning applications: the data quality, type, preprocessing, and management," Journal of Intelligent Manufacturing, Springer, vol. 34(8), pages 3305-3340, December.
    3. Nazanin Hosseini Arian & Alireza Pooya & Fariborz Rahimnia & Ali Sibevei, 2021. "Assessment the effect of rapid prototyping implementation on supply chain sustainability: a system dynamics approach," Operations Management Research, Springer, vol. 14(3), pages 467-493, December.
    4. Foshammer, Jeppe & Søberg, Peder Veng & Helo, Petri & Ituarte, Iñigo Flores, 2022. "Identification of aftermarket and legacy parts suitable for additive manufacturing: A knowledge management-based approach," International Journal of Production Economics, Elsevier, vol. 253(C).
    5. Md Doulotuzzaman Xames & Fariha Kabir Torsha & Ferdous Sarwar, 2023. "A systematic literature review on recent trends of machine learning applications in additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 34(6), pages 2529-2555, August.
    6. Jose M. Framinan & Paz Perez-Gonzalez & Victor Fernandez-Viagas, 2023. "An overview on the use of operations research in additive manufacturing," Annals of Operations Research, Springer, vol. 322(1), pages 5-40, March.

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